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AI pricing: charge for outcomes, not the "AI sticker"
Two pricing conversations this period reached the same conclusion. Carrie Osman of Cruxy says many companies "whack the AI sticker on" and expect to charge 30–50% more. Customers, she says, treat embedded AI such as automated reconciliation as table stakes. A premium is justified only when AI changes the workflow, for example by collapsing six steps into one or replacing a dashboard with a predictive forecast . Her packaging rule: give the ICP everything it needs for a good experience in the base product, and use tiers or add-ons only for specialized capabilities a particular segment values . She also warns that an "agentic calculator with 70 variables" is hard for CFOs and procurement teams to sign off . Her approach to pricing feedback is to weight it by who is giving it and to put little stock in procurement, whose incentive is to push the list price down .
On SaaStr, the hosts gave HubSpot's move to credit pricing as a warning. They estimated that a three-person marketing team would go from about $1,300 to $3,700 a month, mostly in credits for workflow actions . Their guest's alternative is to find the segments where the product already delivers disproportionate value, rather than raising prices on everyone . If an AI feature's product-market fit is still uncertain, he suggests launching it without monetization at first. Otherwise, weak adoption won't tell you whether the product or the price is at fault . When an agent clearly delivers value, though, you should charge for it. One portfolio company launched a vertical agent that doubles ACV, with no free version, and 3% of customers adopted it in the first week .
The same episode made a point about distribution. Their own agents had already chosen Higsfield and Gamma over Canva, and the hosts expect an agent never to suggest switching vendors unless it hits a big blocker . If that holds, products need to be integrated into agent workflows early.
Model PMs: judgment written down as evals
Aakash Gupta reviewed all 46 open model-PM postings. The most common ask was evals, benchmarks or quality metrics (76%), followed by working directly with researchers (63%) and knowing how models are trained (61%). Only 28% asked for a technical degree and 2% for a PhD . The median ask is five years of PM experience, only 8 postings mention Python or SQL, and the median posted base salary is $345K . A Microsoft Copilot post-training posting describes the job as turning feedback like "this response felt too hedgy" into evaluation sets . In a separate note, he argues that every PM should own the AI roadmap for their area. A central "AI strategist" team should not cover up skill gaps among PMs .
G2's Alexis Zheng makes a related argument for agent products in general. You can't fix a probabilistic system one failure at a time, so PMs should design a harness that degrades gracefully and calls for human help when needed . The learning loop is "only as good as the verifiers" . G2 has launched a beta that evaluates customer-support agents in simulated companies, with ground truth taken from real enterprise operations data. Sales is next, and harder to evaluate .
Is the PM job becoming "solution-oriented"?
In r/ProductManagement, one commenter sees startups looking for all-in-one PM/engineer/architect hires. At entry, senior and staff levels, they say, PMs everywhere are turning into feature factories . Another reads "solution-oriented" as code for full-stack PMs who can prototype fast. They argue that the PM's real value is still prioritization and taste, and note that their own company is learning that more features don't bring more adoption . A separate thread described leadership adding last-minute requests to chase competitors or for prestige, treating "a one-shotted PRD" as enough discovery, and wearing down trust with partner teams . One principal-level PM's advice was to force leadership to explicitly kill one priority when three orgs claim the same engineering capacity .
Also notable
- Job market follow-up: Lenny Rachitsky reports 281,000+ open roles at tech companies, up 17% in a year. Open PM, engineering and design roles grew just 2%, while design fell 28% and UX research 45% . The growth is mostly in physical infrastructure such as chips, data centers and hardware .
- Dazzle (Marissa Mayer): the app aims to get a user from opening it to completing a first agent task in 45 seconds or less . Expensive or complicated purchases go to human concierges . Its agents act "on your behalf as opposed to as you," using their own email addresses, phone numbers and payment cards . Hiten Shah describes a broader version of the same trust problem: businesses now need to identify which bots are legitimate customers .
- Stripe announced that Parafin is joining the company, with the aim of expanding revenue opportunities for platforms and small businesses .